Tag: wealth management

The Buy Side is perhaps the biggest segment of Wall St & the financial markets – there are roughly 7,000+ mutual funds, thousands of hedge funds which invest across 40,000 plus instruments – stocks, bonds and other securities. Thus, one of the important business functions on Buy-Side institutional businesses such as Mutual Funds, Hedge Funds, Trusts, Asset Managers, Pension Funds & Private Equity is to constantly analyze a range of information about companies underlying the above instruments to determine their investment worthiness.

The Changing Nature of the Buy Side circa 2018…

When compared with the rest of the financial services industry, the investment and asset management sector has lagged behind in terms of the many business and technology shifts over the recent decades, as we have cataloged in the below series of blogs.

Given the competitive nature of the market, commodified investment strategies will need to rapidly change to incorporate more and more advanced technology into the decision making process.Combined with substandard performance across a crucial sector of the Buy Side – Hedge Funds over the last couple of years – there is all of a sudden a need to incorporate innovative approaches to enhancing Alpha.

This is even more important in this age of real-time information. Market trends, sentiment, and operational risk issues, negative news seem to crop up virtually every day.

All of the above information sources have an ability to dramatically change the quality of an underlying financial instrument. At some point, the ability of a human portfolio manager to keep up with the information onslaught is moot, this calls for techniques around advanced intelligence and automation.

In this blog post, I will discuss key recommendations across the spectrum of Big Data and Artificial Intelligence techniques to help store, process and analyze hundreds of data points across the universe of millions of potential investments.

Recommendation #1 Focus on Non-Traditional Datasets…

Traditional investment management has tended to focus on a financial analysis. This is rigorous fundamental analysis of the investment worthiness of the underlying company. At larger Buy Side firms especially the big mutual funds, tens of Portfolio Managers & Analysts constantly analyze a range of data – both quantitative – e.g. financial statements such as balance sheets, cash flow statements & qualitative – e.g industry trends, supply chain information etc. The trend analysis is typically broken up into three broad areas – Momentum, Value (relative to other players in the same segment) and Future Profitability. It is also not uncommon for large mutual funds to add and remove companies constantly from their investment portfolio – almost on a weekly basis. I propose that firms expand the underlying data into not just the traditional sources identified above but also some of the newer kinds as depicted in the below illustration. The information asymmetry advantage conferred by using a wider source of data has the potential to produce outsize investment performance.

We start moving into the technology now. First, a range of non-traditional data has to be identified and then ingested into a set of commodity servers either in an on-premise data center or using a cloud provider such as Amazon AWS or Microsoft Azure. It then needs to be curated, by applying business level processing. This could include identifying businesses using fundamental analysis or applying algorithms that spot patterns in data that pertain to attractiveness based on certain trending themes etc.

As the below table captures, the advent of Big Data collection, storage, and processing techniques now enable a range of information led capabilities that were simply not possible with older technology.

All of these non-traditional data streams shown above and depicted below can be stored on commodity hardware clusters. This can be done at a fraction of the cost of traditional SAN storage. The combined data can then be analyzed effectively in near real-time thus providing support for advanced business capabilities.

We have covered how the rapidly flowing information across markets creates opportunities for buy-side firms that can exploit this data. In this context, a key capability is to perform backtesting of key algorithmic strategies based on years worth of historical data. These strategies can range from deciding when to trade away exposure to capital optimization. The scale of the analysis problem is immense with virtually 10s of thousands of investment prospects (read companies) operating across the globe across 30+ countries in 6 continents. Every time an algorithm is tweaked, extensive backtesting must be performed on a few quarters or years of historical data to assess its performance.

Big Data has a huge inherent architectural advantage here in that it minimizes data movement & can bring the processing to the data, can cut down the time taken to run these kinds of backtesting and risk analyses across TB of data to hours as opposed to a day or two taken by older technology.

Classification & Class Probability Estimation– For a given set of data, predict for each individual in a population, a discrete set of classes that this individual belongs to. An example classification is – “For all wealth management clients in a given population, who are most likely to respond to an offer to move to a higher segment”. Common techniques used in classification include decision trees, Bayesian models, k-nearest neighbors, induction rules etc. Class Probability Estimation (CPE) is a closely related concept in which a scoring model is created to predict the likelihood that an individual would belong to that class. Employing such classical machine learning techniques such as clustering, segmentation, and classification to create models that can automatically segment investment prospects into key categories. These could be based on certain key investment criteria or factors.

Constantly learning from the underlying data and then ranking companies based on investment metrics and criteria

Adopting Natural language processing (NLP) techniques to read from and to analyze thousands of text documents such as regulatory filings, research reports etc. A key use case is to understand what kinds of geopolitical events can use movements in location sensitive instruments such as heavy metals, commodities such as oil. This is very important as markets move in concert. This can be analyzed on the fly to not just rebalance exposures but also client portfolios. The usecases for NLP are myriad.

Recommendation #5 Leverage Partnerships…

We are aware of the fact that the above investments in technology may be a huge ask of small and mid-level buy-side firms which have viewed technology as a supporting function. However, there now exist service providers that provide the infrastructure, curated data feeds, and custom analytics as a SaaS (Software-as-a-service) to interested clients. Let not size and potential upfront CapEx investment deter these firms from driving their investment methodology to a data-driven process.

Recommendation #6 Increase Automation via Analytics but Human still stays in the loop…

None of the above technology recommendations are intended to displace a portfolio manager who has years of rich industry experience and expertise. The above technology stack can enable these expensive resources to focus their valuable time on activities that add meaningful business value. e.g interviewing key investment prospects, real-time analysis/ portfolio rebalancing, trade execution and management/strategic reporting. Technology is just an aid in that sense and serves as an assistant to the portfolio manager.

Conclusion…

Leading actively managed funds are all about selection, allocation and risk/return assessments. The business goal is to ultimately generate insights that can drive higher investment returns or to shield from investment risk. As Buy Side firms across the board evolve in 2018, one of the key themes from a business and technological standpoint is leveraging AI & Big Data technologies to transform their internal research process from a resource-intensive process to a data-driven investment process.

The previous two posts have covered the business & strategic need for Wealth Management IT applications to reimagine themselves to support their clients. How is this to be accomplished and what does a candidate architectural design pattern look like? What are the key enterprise wide IT concerns? This third & final post (3/3) tackles these questions. An additional following post will return our focus to the business end when we focus on strategic recommendations to industry CXO’s.

The Four Key Business Tenets –

How well a WM firm harness technology determines it’s overall competitive advantage. When advisors can get seamless access to a variety of data, it can help them in manifold ways. For example it helps them make better decisions for their clients as well as make productive use of the day by having the right client data at their fingertips with the push of a button or by means of an intuitive user interface. Similarly, greater access to their portfolios gives clients an more engaging and unified experience.

So, to recap the four strategic goals that WM firms need to operate towards –

Increase Client Loyalty by Digitizing Client Interactions – WM Clients who use services like Uber, Zillow, Amazon etc in their daily lives are now very vocal in demanding a seamless experience across all of the WM services using digital channels. The vast majority of WM applications still lag the innovation cycle, are archaic & are still separately managed. The net issue with this is that the client is faced with distinct user experiences ranging from client onboarding to servicing to transaction management. There is a crying need for IT infrastructure modernization ranging across the industry from Cloud Computing to Big Data to microservices to agile cultures promoting techniques such as a DevOps approach to building out these architectures. Such applications need to provide anticipatory or predictive capabilities at scale while understand the specific customers lifestyles, financial needs & behavioral preferences.

Generate Optimal Client Experiences – In most existing WM systems, siloed functions have led to brittle data architectures operating on custom built legacy applications. This problem is firstly compounded by inflexible core banking systems and secondly exacerbated by a gross lack of standardization in application stacks underlying ancillary capabilities. These factors inhibit deployment flexibility across a range of platforms thus leading to extremely high IT costs and technical debut. The consequence is that these inhibit client facing applications from using data in a manner that constantly & positively impacts the client experience. There is clearly a need to provide an integrated digital experience across a global customer base. And then to offer more intelligent functions based on existing data assets. Current players do possess a huge first mover advantage as they offer highly established financial products across their large (and largely loyal & sticky) customer bases, a wide networks of physical locations, rich troves of data that pertain to customer accounts & demographic information. However, it is not enough to just possess the data. They must be able to drive change through legacy thinking and infrastructures as things change around the entire industry as it struggles to adapt to a major new segment – the millenials – who increasingly use mobile devices and demand more contextual services as well as a seamless and highly analytic driven & unified banking experience – akin to what they commonly experience via the internet – at web properties like Facebook, Amazon, Google or Yahoo etc

Automate Back & Mid Office Processes Across the WM Value Chain – The needs to forge a closer banker/client experience is not just driving demand around data silos & streams themselves but also forcing players to move away from paper based models to more of a seamless, digital & highly automated model to rework a ton of existing back & front office processes – which is the weakest link in the chain. These processes range from risk data aggregation, supranational compliance (AML,KYC, CRS & FATCA), financial reporting across a range of global regions & Cyber Security. Can the Data architectures & the IT systems that leverage them be created in such a way that they permit agility while constantly learning & optimizing their behaviors across national regulations, InfoSec & compliance requirements? Can every piece of actionable data be aggregated,secured, transformed and reported on in such a way that it’s quality across the entire lifecycle is guaranteed?

Tune existing business models based on client tastes and feedback – While Automation 1.0 focuses on digitizing processes, rules & workflow as stated above; Automation 2.0 implies strong predictive modeling capabilities working at large scale – systems that constantly learn and optimize products & services based on client needs & preferences. The clear ongoing theme in the WM space is constant innovation. Firms need to ask themselves if they are offering the right products that cater to an increasingly affluent yet dynamic clientele. This is the area where firms need to show that they can compete with the FinTechs (Wealthfront, Nutmeg, Fodor Bank et al) to attract younger customers.

Now that we have covered the business bases, what foundational technology choices comprise the satisfaction of the above? Lets examine that first at a higher level and then in more detail.

Ten Key Overall System Architecture Tenets –

The design and architecture of a solution as large and complex as a WM enterprise is a multidimensional challenge. The below illustration catalogs the four foundational capabilities of such a system – Omnichannel, Mobile Native Experiences, Massive Data processing capabilities, Cloud Computing & Predictive Analytics – all operating at scale.

Illustration – Top Level Architectural Components

Here are some of the key global design characteristics for a common architecture framework :

The Architecture shall support a high degree of data agility and data intelligence. The end goal being that that every customer click, discussion & preference shall drive an analytics infused interaction between the advisor and the client

The Architecture shall support algorithmic capabilities that enable the creation of new services like automated (or Robo) advisors

The Architecture shall support a very high degree of scale across many numbers of users, interactions & omni-channel transactions while working across global infrastructure

The Architecture shall support deployment across cost efficient platforms like a public or private cloud. In short, the design of the application shall not constrain the available deployment options – which may vary because of cost considerations. The infrastructure options supported shall range from virtual machines to docker based containers – whether running on a public cloud, private cloud or in a hybrid cloud

The Architecture shall support small, incremental changes to business services & data elements based on changing business requirements

The Architecture shall support standardization across application stacks, toolsets for development & data technology to a high degree

The Architecture shall support the creation of a user interface that is highly visual and feature rich from a content standpoint when accessed across any device

The Architecture shall support an API based model to invoke any interaction – by a client or an advisor or a business partner

The Architecture shall support the development and deployment of an application that encourages a DevOps based approach

The Architecture shall support the easy creation of scalable business processes that natively emit business metrics from the time they’re instantiated to throughout their lifecycle

Given the above list of requirements – the application architecture that is a “best fit” is shown below.

Cloud Computing across it’s three main delivery models (IaaS, PaaS & SaaS) is largely a mainstream endeavor in financial services and no longer an esoteric adventure only for brave innovators. A range of institutions are either deploying or testing cloud-based solutions that span the full range of cloud delivery models. These capabilities include –

Choosing Cloud based infrastructure – whether that is secure public cloud (Amazon AWS or Microsoft Azure) or an internal private cloud (OpenStack etc) or even a hybrid approach is a safe and sound bet for WM applications. Business innovation and transformation are best enabled by a cloud based infrastructure.

Data Tier –

While banking data tiers are usually composed of different technologies like RDBMS, EDW (Enterprise Data Warehouses), CMS (Content Management Systems) & Big Data etc. My recommendation for the target state is largely dominated by a Big Data Platform powered by Hadoop. Given the focus of the digital Wealth Manager to leverage algorithmic asset management and providing predictive analytics to create tailored & managed portfolios for their clients – Hadoop is a natural fit as it is fast emerging as the platform of choice for analytic applications.

The reasons for choosing Hadoop as the dominant technology in the data tier are the below –

Hadoop’s ability to ingest and work with all the above kinds of data & more (using the schema on read method) has been proven at massive scale. Operational data stores are being built on Hadoop at a fraction of the cost & effort involved with older types of data technology (RDBMS & EDW)

The ability to perform multiple types of processing on a given data set. This processing varies across batch, streaming, in memory and realtime which greatly opens up the ability to create, test & deploy closed loop analytics quicker than ever before

The DAS (Direct Attached Storage) model that Hadoop provides fits neatly in with the horizontal scale out model that the services, UX and business process tier leverage. This keeps cuts Capital Expenditure to a bare minimum.

The ability to retain data for long periods of time thus providing WM applications with predictive models that can reason on historical data

Hadoop provides the ability to run a massive volumes of models in a very short amount of time helps with modeling automation

Due to it’s parallel processing nature, Hadoop can run calculations (pricing, risk, portfolio, reporting etc) in minutes versus the hours it took using older technology

Hadoop has to work with existing data investments and to augment them with data ingestion & transformation leaving EDW’s to perform complex analytics that they excel at – a huge bonus.

Services Tier –

The overall goals of the services tier are to help design, develop,modify and deploy business components in such a way that overall WM application delivery follows a continuous delivery/deployment (CI/CD) paradigm.Given that WM Platforms are some of the most complex financial applications out there, this also has the ancillary benefit of leaving different teams – digital channels, client on boarding, bill pay, transaction management & mid/back office teams to develop and update their components largely independent of other teams. Thus a large monolithic WM enterprise platform is decomposed into its constituent services which are loosely coupled and each is focused on one independent & autonomous business task only. The word ‘task’ here referring to a business capability that has tangible business value.

A highly scalable, open source & industry leading platform as a service (PaaS) like Red Hat’s OpenShift is recommended as the way of building out and hosting this tier. Microservices have moved from the webscale world to fast becoming the standard for building mission critical applications in many industries. Leveraging a PaaS such as OpenShift provides a way to help cut the “technical debt” that has plagued both developers and IT Ops. OpenShift provides the right level of abstraction to encapsulate microservices via it’s native support for Docker Containers. This also has the concomitant advantage of standardizing application stacks, streamlining deployment pipelines thus leading the charge to a DevOps style of building applications.

Further I recommend that service designer take the approach that their micro services can be deployed in a SaaS application format going forward – which usually implies taking an API based approach.

Though segments of the banking industry have historically been early adopters of analytics, the wealth management space has largely been a laggard. However, the large datasets that are prevalent in WM as well as the need to drive customer interaction & journeys, risk & compliance reporting, detecting fraud etc calls for a strategic relook at this space.

Techniques like Machine Learning, Data Science & AI feed into core business processes thus improving them. For instance, Machine Learning techniques support the creation of self improving algorithms which get better with data thus making accurate business predictions. Thus, the overarching goal of the analytics tier should be to support a higher degree of automation by working with the business process and the services tier. Predictive Analytics can be leveraged across the value chain of WM – ranging from new customer acquisition to customer journey to the back office. More recently these techniques have found increased rates of adoption with enterprise concerns from cyber security to telemetry data processing.

Though most large banks do have pockets of BPM implementations that are adding or beginning to add significant business value, an enterprise-wide re-look at the core revenue-producing activities is called for, as is a deeper examination of driving competitive advantage. BPM now has evolved into more than just pure process management. Meanwhile, other disciplines have been added to BPM — which has now become an umbrella term. These include business rules management, event processing, and business resource planning.

WM firms are fertile ground for business process automation, since most managers across their various lines of business are simply a collection of core and differentiated processes. Examples are private banking (with processes including onboarding customers, collecting deposits, conducting business via multiple channels, and compliance with regulatory mandates such as KYC and AML); investment banking (including straight-through-processing, trading platforms, prime brokerage, and compliance with regulation); payment services; and portfolio management (including modeling model portfolio positions and providing complete transparency across the end-to-end life cycle). The key takeaway is that driving automation can result not just in better business visibility and accountability on behalf of various actors. It can also drive revenue and contribute significantly to the bottom line.

A business process system should allow an IT analyst or customer or advisor to convey their business process by describing the steps that need to be executed in order to achieve the goal (and explain the order of those steps, typically using a flow chart). This greatly improves the visibility of business logic, resulting in higher-level and domain-specific representations (tailored to finance) that can be understood by business users and should be easier to monitor by management. Again , leveraging a PaaS such as OpenShift in conjunction with an industry leading open source BPMS (Business Process Management System) such as JBOSS BPMS provides an integrated BPM capability that can create cloud ready and horizontally scalable business processes.

User Experience Tier –

The UX (User Experience) tier fronts humans – client. advisor, regulator, management and other business users across all touchpoints. An API tier is provided for partner applications and other non-human actors to interact with business service tier.

The UX tier has the following global responsibilities –

Provide a consistent user experience across all channels (mobile, eBanking, tablet etc) in a way that is a seamless and non-siloded view. The implication is that clients should be able to begin a business transaction in channel A and be able to continue them in channel B where it makes business sense.

Understand client personas and integrate with the business & predictive analytic tier in such a way that the UX is deeply integrated with the overall information architecture

Increased & relevant data volumes in turn help improve predictive capabilities of customer models as they can constantly be harnessed to drive higher insight and visibility into a range of areas – client tastes, product fit & business strategy

These in turn provide valuable insights to drive improvements in products & services

Rinse and Repeat – constantly optimize and learn on the go

This cycle needs to be accelerated helping the creation of a learning organization which can outlast competition by means of a culture of unafraid experimentation and innovation.

Summary

New Age technology platforms designed around the four key business needs (Client experience, Advisor productivity, a highly Automated backoffice & a culture of constant innovation) will create immense operational efficiency, better business models, increased relevance and ultimately drive revenues. These will separate the visionaries, leaders from the laggards in the years to come.

The first post in this three part series brought to the fore critical strategic trends in the Wealth & Asset Management (WM) space – the most lucrative portion of Banking. This second post will describe an innovation framework for a forward looking WM institution.We will do this by means of concrete & granular use cases across the entire WM business lifecycle. The final post will cover technology architecture and business strategy recommendations for WM CXO’s.

Introduction:

The ability to sign up wealthy individuals & families; then retaining them over the years by offer those engaging, bespoke & contextual services will largely provide growth in the Wealth Management industry in 2016 and beyond. However, WM as an industry sector has lagged other areas within banking from a technology & digitization standpoint.Multiple business forces ranging from increased regulatory & compliance demands, technology savvy customers and new Age FinTechs have led to firms slowly begin a makeover process.

So all of this begs the question – what do WM need to do to grow their client base and ultimately revenues? I contend that there are four strategic goals that firms need to operate across –

Increase Client Loyalty by Digitizing Client Interactions – WM Clients who use services like Uber, Zillow, Amazon etc in their daily lives are now very vocal in demanding a seamless experience across all of the WM services using digital channels. The vast majority of WM applications still lag the innovation cycle, are archaic & are still separately managed. The net issue with this is that the client is faced with distinct user experiences ranging from client onboarding to servicing to transaction management. There is a crying need for IT infrastructure modernization ranging across the industry from Cloud Computing to Big Data to microservices to agile cultures promoting techniques such as a DevOps approach to building out these architectures. Such applications need to provide anticipatory or predictive capabilities at scale while understand the specific customers lifestyles, financial needs & behavioral preferences.

Generate Optimal Client Experiences – In most existing WM systems, siloed functions have led to brittle data architectures operating on custom built legacy applications. This problem is firstly compounded by inflexible core banking systems and secondly exacerbated by a gross lack of standardization in application stacks underlying ancillary capabilities. These factors inhibit deployment flexibility across a range of platforms thus leading to extremely high IT costs and technical debut. The consequence is that these inhibit client facing applications from using data in a manner that constantly & positively impacts the client experience. There is clearly a need to provide an integrated digital experience across a global customer base. And then to offer more intelligent functions based on existing data assets. Current players do possess a huge first mover advantage as they offer highly established financial products across their large (and largely loyal & sticky) customer bases, a wide networks of physical locations, rich troves of data that pertain to customer accounts & demographic information. However, it is not enough to just possess the data. They must be able to drive change through legacy thinking and infrastructures as things change around the entire industry as it struggles to adapt to a major new segment – the millenials – who increasingly use mobile devices and demand more contextual services as well as a seamless and highly analytic driven & unified banking experience – akin to what they commonly experience via the internet – at web properties like Facebook, Amazon, Google or Yahoo etc

Automate Back & Mid Office Processes Across the WM Value Chain – The needs to forge a closer banker/client experience is not just driving demand around data silos & streams themselves but also forcing players to move away from paper based models to more of a seamless, digital & highly automated model to rework a ton of existing back & front office processes – which is the weakest link in the chain. These processes range from risk data aggregation, supranational compliance (AML,KYC, CRS & FATCA), financial reporting across a range of global regions & Cyber Security. Can the Data architectures & the IT systems that leverage them be created in such a way that they permit agility while constantly learning & optimizing their behaviors across national regulations, InfoSec & compliance requirements? Can every piece of actionable data be aggregated,secured, transformed and reported on in such a way that it’s quality across the entire lifecycle is guaranteed?

Tune existing business models based on client tastes and feedback – While Automation 1.0 focuses on digitizing processes, rules & workflow as stated above; Automation 2.0 implies strong predictive modeling capabilities working at large scale – systems that constantly learn and optimize products & services based on client needs & preferences. The clear ongoing theme in the WM space is constant innovation. Firms need to ask themselves if they are offering the right products that cater to an increasingly affluent yet dynamic clientele. This is the area where firms need to show that they can compete with the FinTechs (Wealthfront, Nutmeg, Fodor Bank et al) to attract younger customers.

Having set the stage for the capabilities that need to be added or augmented, let us examine what the WM firm of the future can look like.

Illustration – Technology Driven Wealth Management

Improve the Client Experience

The ability of the clients to view their holistic portfolio, banking,bill pay data & advisor interactions in one intuitive user interface is a must have. All this information needs to be available across multiple channels of banking & across all accounts the client owns with multiple FIs (Financial Institutions). Further, pulling in data from relevant social media properties like Twitter, Facebook etc can help clients gauge the popularity of certain products across their networks thus helping them make targeted, real-time, decisions that increase market share. Easy access to investment advice, portfolio analytics and DIY (Do it Yourself) “what if” scenarios based on the client’s investment profile, past financial behavior & family commitments are highly desirable and encourage client loyalty.

Help the Advisor –

On the other side of the coin, most WM advisors lack a comprehensive view of their customers. This is due to legacy IT reasons due to which their interactions with clients across multiple channels takes up a lot of their work time but also results in limited collaboration within the bank when servicing client needs.

Other “must have” capabilities –

Predicting Customer Attrition & Churn across both a single client as well as over a n advisor’s entire client base

Run in place analytics on customer lifetime value (CLV) and yield per customer

Suggest Next Best Action for a given client and across a pool of managed clients

Provide multiple levels of dashboards ranging from the Descriptive (Business Intelligence) to the Prescriptive (business simulation as well as optimization)

Digitize Business Processes –

Since a high degree of WM technology still lives in the legacy age, it should not be a surprise that a lot of backend processes result in client dissatisfaction as well as an inability to provide lean & efficient operations. Strategic investments in Business Process Management (BPM) systems, Big Data architectures & processing techniques, Digital Signature systems & augmenting tactical document management systems can result in a high degree of digitization. This leads to seamless business interoperability, efficient client operations and an ability to turn around compliance information quickly & more efficiently over to regulatory authorities.

Invest In Technology to Drive the Business –

Strategic deployment of technology assets will be the differentiator in the WM business going forward. The technology investments that WM firms need to make are in three broad areas – Big Data & Predictive Analytics, Cloud Computing & in a DevOps based approach to building out these capabilities.

Performance Management Metrics for the business across client segments, advisors and specific geographies

Better Client Advice based on portfolio optimization which takes client life journey details into account as opposed to static age based rebalancing

Promoting client’s ability to self service their accounts thus reducing load on advisors for mundane tasks

The biggest (and perhaps the most famous) capability is providing Robo Advisor functionality with advanced visualization capabilities. One of the goals here is to compete with Fintechs which are automating their customer account servicing using an automated approach.

Help with Compliance and other reporting functions

Big Data and Hadoop seems to be emerging as the platform of choice for many reasons – ability to handle any kind of data at scale, cost, techniques like deep learning need a lot of computing power which Hadoop can provide via paralleization, integration with SAS/Python and R. A high degree of data preprocessing could be done via Advanced MapReduce techniques.Finally, additive to all of this is an agile infrastructure based on cloud computing principles which calls out for a microservice based approach to building out software architectures, mobile platforms that accelerate customers abilities to bank from anywhere. DevOps dictates an increased focus on automation from a business process to software system delivery and encourages a culture that encourages risk taking & a “fail fast” approach.

The final post in this series will cover a high level technology architecture and then specific recommendations to WM CXO’s.